Network model diagnostics - balanced statistics
Load packages and model fits
rm(list = ls())
suppressMessages(library("EpiModelHIV"))
library("latticeExtra")## Loading required package: lattice## Loading required package: RColorBrewerlibrary("knitr")
library("kableExtra")
library("here")## here() starts at /homes/dpwhite/R/GitHub Repos/WHAMPload(file = here("Model fits and simulations/Fit tests and debugging/est/est.p.bal.bdmaxout.rda"))
load(file = here("Model fits and simulations/Fit tests and debugging/est/est.p.bal.bdmaxout_longdur.rda"))Persistent model
This model uses balanced statistics for racial/ethnic mixing and race/ethnicity-specific degree, imposes the constraint on degree using the argument: constraints = ~bd(maxout = 2), and assumes 80% regional homophily.
Model terms and control settings
Terms and constraints| Terms and constraints | Full model |
|---|---|
| edges | 2017.5 |
| nodefactor.deg.main.1 | 1699.0 |
| nodefactor.race..wa.B | 285.5 |
| nodefactor.race..wa.H | 605.3 |
| nodefactor.region.EW | 368.4 |
| nodefactor.region.OW | 1178.3 |
| concurrent | 1384.0 |
| nodematch.race..wa.B | 8.5 |
| nodematch.race..wa.H | 51.2 |
| nodematch.race..wa.O | 1247.1 |
| nodematch.region | 1614.0 |
| absdiff.sqrt.age | 1664.8 |
| nodematch.role.class.I | -Inf |
| nodematch.role.class.R | -Inf |
Control settings
set.control.ergm = control.ergm(MCMC.interval = 1e+5,
MCMC.samplesize = 7500,
MCMC.burnin = 1e+6,
MPLE.max.dyad.types = 1e+7,
init.method = "zeros",
MCMLE.maxit = 400,
parallel = np/2,
parallel.type="PSOCK"))MCMC diagnostics
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 1.548867 58.445 0.33743 0.38157
## nodefactor.deg.main.1 1.475900 61.057 0.35251 0.39222
## nodefactor.race..wa.B 0.040400 19.617 0.11326 0.13388
## nodefactor.race..wa.H -0.083200 29.372 0.16958 0.20240
## nodefactor.region.EW -0.411438 29.210 0.16865 0.23093
## nodefactor.region.OW 0.933529 58.208 0.33607 0.38188
## concurrent 1.183967 52.259 0.30172 0.34581
## nodematch.race..wa.B -0.006349 2.968 0.01714 0.01858
## nodematch.race..wa.H -0.202530 7.318 0.04225 0.05724
## nodematch.race..wa.O 1.273187 44.637 0.25771 0.28210
## nodematch.region 1.332333 50.098 0.28924 0.33585
## absdiff.sqrt.age 1.367963 57.792 0.33366 0.35305
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -113.50 -38.500 1.5000 41.500 115.52
## nodefactor.deg.main.1 -117.00 -40.000 2.0000 43.000 122.00
## nodefactor.race..wa.B -37.52 -13.517 -0.5168 13.483 39.48
## nodefactor.race..wa.H -57.34 -19.340 -0.3400 19.660 57.66
## nodefactor.region.EW -56.37 -20.375 -0.3750 18.625 57.63
## nodefactor.region.OW -111.29 -38.294 0.7064 39.706 115.71
## concurrent -101.00 -34.000 1.0000 37.000 105.00
## nodematch.race..wa.B -5.48 -2.480 -0.4798 1.520 6.52
## nodematch.race..wa.H -14.18 -5.181 -0.1815 4.819 14.82
## nodematch.race..wa.O -86.08 -29.081 0.9192 30.919 88.92
## nodematch.region -97.00 -33.000 1.0000 35.000 100.00
## absdiff.sqrt.age -111.33 -37.730 0.9672 40.542 115.06
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.main.1
## edges 1.00000000 0.81519318
## nodefactor.deg.main.1 0.81519318 1.00000000
## nodefactor.race..wa.B 0.40503515 0.31112647
## nodefactor.race..wa.H 0.53802595 0.47622239
## nodefactor.region.EW 0.38091436 0.30878302
## nodefactor.region.OW 0.60910688 0.43838159
## concurrent 0.95349830 0.77658471
## nodematch.race..wa.B 0.08230671 0.06055628
## nodematch.race..wa.H 0.17241480 0.16303553
## nodematch.race..wa.O 0.84599321 0.67658119
## nodematch.region 0.93150548 0.76598404
## absdiff.sqrt.age 0.84546930 0.69182210
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.40503515 0.53802595
## nodefactor.deg.main.1 0.31112647 0.47622239
## nodefactor.race..wa.B 1.00000000 0.18429240
## nodefactor.race..wa.H 0.18429240 1.00000000
## nodefactor.region.EW 0.09986203 0.33512114
## nodefactor.region.OW 0.20396421 0.32201444
## concurrent 0.39360742 0.52073233
## nodematch.race..wa.B 0.35701258 0.01803472
## nodematch.race..wa.H 0.02233321 0.56113044
## nodematch.race..wa.O 0.08557663 0.11661057
## nodematch.region 0.38903165 0.48354171
## absdiff.sqrt.age 0.34476537 0.46143846
## nodefactor.region.EW nodefactor.region.OW concurrent
## edges 0.38091436 0.60910688 0.95349830
## nodefactor.deg.main.1 0.30878302 0.43838159 0.77658471
## nodefactor.race..wa.B 0.09986203 0.20396421 0.39360742
## nodefactor.race..wa.H 0.33512114 0.32201444 0.52073233
## nodefactor.region.EW 1.00000000 0.09449989 0.36020483
## nodefactor.region.OW 0.09449989 1.00000000 0.57090743
## concurrent 0.36020483 0.57090743 1.00000000
## nodematch.race..wa.B 0.01637754 0.03052985 0.07977151
## nodematch.race..wa.H 0.17350885 0.09804043 0.16891748
## nodematch.race..wa.O 0.27793250 0.53156799 0.80000612
## nodematch.region 0.25626853 0.53586318 0.89034685
## absdiff.sqrt.age 0.32410742 0.51343425 0.80363920
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.082306711 0.172414802
## nodefactor.deg.main.1 0.060556276 0.163035533
## nodefactor.race..wa.B 0.357012583 0.022333213
## nodefactor.race..wa.H 0.018034721 0.561130442
## nodefactor.region.EW 0.016377540 0.173508853
## nodefactor.region.OW 0.030529850 0.098040433
## concurrent 0.079771506 0.168917482
## nodematch.race..wa.B 1.000000000 0.005545926
## nodematch.race..wa.H 0.005545926 1.000000000
## nodematch.race..wa.O 0.013055551 0.017585075
## nodematch.region 0.083819696 0.151575141
## absdiff.sqrt.age 0.068140920 0.150874369
## nodematch.race..wa.O nodematch.region
## edges 0.84599321 0.9315055
## nodefactor.deg.main.1 0.67658119 0.7659840
## nodefactor.race..wa.B 0.08557663 0.3890316
## nodefactor.race..wa.H 0.11661057 0.4835417
## nodefactor.region.EW 0.27793250 0.2562685
## nodefactor.region.OW 0.53156799 0.5358632
## concurrent 0.80000612 0.8903469
## nodematch.race..wa.B 0.01305555 0.0838197
## nodematch.race..wa.H 0.01758507 0.1515751
## nodematch.race..wa.O 1.00000000 0.7931937
## nodematch.region 0.79319375 1.0000000
## absdiff.sqrt.age 0.71145296 0.7861505
## absdiff.sqrt.age
## edges 0.84546930
## nodefactor.deg.main.1 0.69182210
## nodefactor.race..wa.B 0.34476537
## nodefactor.race..wa.H 0.46143846
## nodefactor.region.EW 0.32410742
## nodefactor.region.OW 0.51343425
## concurrent 0.80363920
## nodematch.race..wa.B 0.06814092
## nodematch.race..wa.H 0.15087437
## nodematch.race..wa.O 0.71145296
## nodematch.region 0.78615051
## absdiff.sqrt.age 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.113304475 0.1077862412 0.085685880
## Lag 2e+05 0.046440380 0.0427764145 0.032557893
## Lag 3e+05 0.001889311 0.0005402302 -0.003269011
## Lag 4e+05 -0.014684152 -0.0127755195 -0.014740998
## Lag 5e+05 -0.026558324 -0.0337196439 -0.015783454
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.00000000 1.000000000
## Lag 1e+05 0.1551450307 0.24644427 0.168338150
## Lag 2e+05 0.0909567133 0.12492632 0.045713156
## Lag 3e+05 0.0256127315 0.07819797 0.016216972
## Lag 4e+05 0.0083827285 0.02322089 -0.004436028
## Lag 5e+05 0.0008292094 0.02083720 0.018740995
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.116828997 0.064091912 0.22764137
## Lag 2e+05 0.040087138 0.012428641 0.10542686
## Lag 3e+05 0.009518609 0.021132655 0.03618976
## Lag 4e+05 -0.002417834 -0.006700641 0.02589770
## Lag 5e+05 -0.023625296 -0.012224641 0.03096766
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.0000000000 1.0000000000 1.00000000
## Lag 1e+05 0.0997007667 0.1357826863 0.07467937
## Lag 2e+05 0.0043450167 0.0510066755 0.03203095
## Lag 3e+05 -0.0004859658 0.0041245619 -0.01457255
## Lag 4e+05 -0.0084531285 -0.0006973199 -0.02731021
## Lag 5e+05 -0.0021224438 -0.0169324965 -0.03313530
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.123622041 0.107447334 0.13035595
## Lag 2e+05 0.045634587 0.054512062 0.05035960
## Lag 3e+05 -0.027010022 -0.008979739 0.03078033
## Lag 4e+05 0.002517576 0.003993799 0.02069027
## Lag 5e+05 -0.027482063 0.000975688 0.01431919
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.110093258 0.236286284 0.118147829
## Lag 2e+05 0.039325989 0.096732907 0.013024208
## Lag 3e+05 -0.022371341 0.034329503 -0.009275224
## Lag 4e+05 0.001206407 0.006317831 0.008338183
## Lag 5e+05 -0.016913547 0.004253337 0.001436349
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.12554212 0.102109182 0.20720073
## Lag 2e+05 0.04613891 0.010491448 0.10163479
## Lag 3e+05 -0.02386385 -0.002231828 0.03203394
## Lag 4e+05 0.01177111 0.010016460 0.01254258
## Lag 5e+05 -0.02591007 -0.003839013 0.01681272
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.10165262 0.148120488 0.07656400
## Lag 2e+05 0.02124252 0.035323564 0.01606162
## Lag 3e+05 -0.02022219 -0.016813801 -0.01961923
## Lag 4e+05 -0.01185846 0.006849289 0.00204948
## Lag 5e+05 -0.03459688 -0.022720420 -0.03804253
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.00000000 1.000000000
## Lag 1e+05 0.1089290671 0.11456162 0.134384117
## Lag 2e+05 0.0392160977 0.04130439 0.038340908
## Lag 3e+05 0.0020005524 0.02133518 -0.001200751
## Lag 4e+05 0.0014915933 -0.00374739 -0.016692543
## Lag 5e+05 -0.0009304538 -0.00240573 0.004934471
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.15619034 0.237447716 0.12363441
## Lag 2e+05 0.03564674 0.107644076 0.03907065
## Lag 3e+05 0.04231326 0.023611622 0.02833126
## Lag 4e+05 0.01094877 0.011799928 -0.01551311
## Lag 5e+05 -0.01446536 -0.001324968 -0.01165691
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.00000000
## Lag 1e+05 0.121087980 0.0971308880 0.23001839
## Lag 2e+05 0.040342613 0.0027193723 0.06456966
## Lag 3e+05 0.007984078 -0.0053702791 0.06566798
## Lag 4e+05 -0.006610696 -0.0129771172 0.03060214
## Lag 5e+05 -0.001806375 0.0001212292 0.01224619
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.081008036 0.132696179 0.0561114173
## Lag 2e+05 0.021562601 0.050894134 0.0261595383
## Lag 3e+05 -0.005813084 0.004934040 -0.0071150953
## Lag 4e+05 0.012916261 0.008321204 0.0009875365
## Lag 5e+05 -0.007994821 0.001409305 0.0140377460
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.121069813 0.0914789619 0.137648296
## Lag 2e+05 0.040233368 0.0149954357 0.057778605
## Lag 3e+05 0.018502505 0.0155550031 0.001193084
## Lag 4e+05 -0.003597699 0.0162892069 0.036157564
## Lag 5e+05 -0.006818822 -0.0008187687 0.032430695
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.00000000 1.000000000
## Lag 1e+05 0.1481503146 0.25037260 0.141061919
## Lag 2e+05 0.0193306844 0.13095549 0.036308357
## Lag 3e+05 -0.0009231131 0.05424337 -0.016983407
## Lag 4e+05 0.0248096492 0.01889793 -0.001236598
## Lag 5e+05 -0.0292906966 0.00301317 0.010866048
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.125545283 0.071042435 0.22401954
## Lag 2e+05 0.041686791 -0.022022027 0.09521619
## Lag 3e+05 0.021776584 0.000133624 0.05656271
## Lag 4e+05 -0.007367371 0.024851808 0.06626511
## Lag 5e+05 -0.018286669 -0.003518829 0.01337842
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.0000000000 1.00000000 1.000000000
## Lag 1e+05 0.0889694919 0.15087323 0.083050205
## Lag 2e+05 0.0337071811 0.04463543 0.007814751
## Lag 3e+05 0.0242440662 0.01534799 0.015687528
## Lag 4e+05 -0.0009985509 -0.02390200 0.006428365
## Lag 5e+05 0.0040747896 -0.01296205 0.006035271
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000e+00 1.000000000 1.000000000
## Lag 1e+05 1.216592e-01 0.113165403 0.113971690
## Lag 2e+05 2.732644e-02 0.012065365 0.036779749
## Lag 3e+05 9.499234e-03 -0.007474945 -0.008920865
## Lag 4e+05 -1.113893e-05 0.004655816 0.017758279
## Lag 5e+05 2.492988e-02 0.024139511 0.027643871
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.18288392 0.25688514 0.126293782
## Lag 2e+05 0.07335358 0.09107081 0.063258228
## Lag 3e+05 0.02339376 0.05102729 0.045267000
## Lag 4e+05 0.02653655 0.04766948 -0.006004868
## Lag 5e+05 0.01897655 0.03387014 -0.024456085
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.127986832 0.082682440 0.268040259
## Lag 2e+05 0.018166604 0.005349053 0.112044264
## Lag 3e+05 0.002813893 -0.003216923 0.034597751
## Lag 4e+05 0.006044847 0.017903233 0.007309575
## Lag 5e+05 0.032638021 -0.016111198 0.014573401
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.1081743352 0.137260768 0.078211293
## Lag 2e+05 0.0377068198 0.033467706 -0.005731664
## Lag 3e+05 0.0179842838 0.009109380 -0.000541897
## Lag 4e+05 -0.0014075299 0.005220852 -0.012673478
## Lag 5e+05 -0.0004859287 0.025643982 0.011925375
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.116147471 0.0870390154 0.130109428
## Lag 2e+05 0.032207306 0.0140174621 0.054297242
## Lag 3e+05 -0.001439004 -0.0061906270 0.009268803
## Lag 4e+05 -0.032231998 -0.0323008133 0.001158814
## Lag 5e+05 0.001405979 0.0007662966 0.006621072
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.123842023 0.23631193 0.123994846
## Lag 2e+05 0.026309998 0.10209516 0.023637078
## Lag 3e+05 -0.002366284 0.06119505 -0.010870672
## Lag 4e+05 -0.005550779 0.02545613 -0.036353708
## Lag 5e+05 0.007399319 0.02705030 -0.002657763
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.12485510 0.071498537 0.226789855
## Lag 2e+05 0.03531041 0.019268354 0.092388640
## Lag 3e+05 0.01004009 0.002426208 0.020851556
## Lag 4e+05 -0.02996580 0.035496558 0.007546680
## Lag 5e+05 0.01091708 -0.006514005 0.003372839
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.0000000000 1.000000000 1.00000000
## Lag 1e+05 0.0954868120 0.145897106 0.04020434
## Lag 2e+05 0.0198789944 0.028310149 0.02331869
## Lag 3e+05 0.0100193401 0.004107930 -0.01054870
## Lag 4e+05 -0.0201045253 -0.027018421 -0.02593996
## Lag 5e+05 0.0008372097 -0.004366759 0.02061838
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 0.0737408893 0.062394153 0.1322080219
## Lag 2e+05 0.0478959652 0.052806750 0.0419309081
## Lag 3e+05 -0.0050821904 -0.004216709 -0.0219826527
## Lag 4e+05 0.0030631282 -0.013855424 -0.0088073566
## Lag 5e+05 0.0007798496 0.005348116 0.0005568336
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.134555013 0.23427886 0.12419771
## Lag 2e+05 0.055948476 0.10919746 0.04495236
## Lag 3e+05 0.023231268 0.05419287 0.01094709
## Lag 4e+05 -0.004697979 0.02585209 0.01597054
## Lag 5e+05 -0.009409586 -0.00827977 0.01969711
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0833105752 0.104934919 0.267002115
## Lag 2e+05 0.0440770877 0.018043733 0.110209101
## Lag 3e+05 -0.0031352103 -0.054937911 0.049835035
## Lag 4e+05 -0.0001436757 0.008431626 0.006553608
## Lag 5e+05 -0.0020998099 -0.012331004 0.014937921
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.041840707 0.086906224 0.050178910
## Lag 2e+05 0.038254503 0.038085804 0.036331341
## Lag 3e+05 0.011146859 -0.010618118 -0.036976515
## Lag 4e+05 0.009870405 0.006465944 -0.026829622
## Lag 5e+05 0.003309830 0.002659854 0.005024585
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.119879264 0.081482249 0.135932116
## Lag 2e+05 0.026911811 0.024634489 0.025966844
## Lag 3e+05 -0.002127624 -0.004617026 0.024494330
## Lag 4e+05 -0.007634271 -0.009812751 0.008563000
## Lag 5e+05 -0.047814689 -0.025758913 -0.003838996
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.0000000000
## Lag 1e+05 0.147685088 0.22309491 0.1405523149
## Lag 2e+05 0.054715284 0.10639457 0.0621098106
## Lag 3e+05 0.013058731 0.05273512 0.0105424326
## Lag 4e+05 0.016658368 0.02421394 -0.0003176726
## Lag 5e+05 0.002485204 0.01382888 -0.0392197651
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.00000000
## Lag 1e+05 0.1299912865 0.122708755 0.23676682
## Lag 2e+05 0.0216743542 0.021577059 0.12128202
## Lag 3e+05 0.0004806554 0.009819700 0.05028919
## Lag 4e+05 -0.0125639295 0.008099762 0.03690155
## Lag 5e+05 -0.0348469799 0.037772749 0.03232354
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.08443574 0.150312522 0.078405464
## Lag 2e+05 0.02837672 0.028113018 -0.004579688
## Lag 3e+05 -0.00921807 -0.004717825 -0.006530061
## Lag 4e+05 -0.01007085 -0.017897893 0.007267201
## Lag 5e+05 -0.04816651 -0.044274549 -0.034854995
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -1.9196 -1.5121 -0.2316
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -3.6108 -1.3091 -1.1427
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -1.6913 0.5936 -1.8831
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -0.3328 -1.8248 -0.7677
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.0549139087 0.1305107081 0.8168821719
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.0003053088 0.1905038965 0.2531816972
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.0907814187 0.5528041934 0.0596908511
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.7392907996 0.0680263501 0.4426842197
## Joint P-value (lower = worse): 0.05757818 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 1.0217 1.8272 -0.6941
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.9418 2.0610 0.8475
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.9068 1.8988 0.4293
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 1.1319 0.6485 0.4730
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.30690401 0.06766789 0.48763872
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.34627989 0.03930571 0.39670776
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.36452813 0.05759102 0.66770844
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.25766122 0.51668858 0.63619970
## Joint P-value (lower = worse): 0.1341088 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.53346 1.08731 1.30161
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 3.12752 -0.16080 0.01196
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.51051 -0.36056 3.62158
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -0.72810 0.13689 0.49006
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.5937173309 0.2769000486 0.1930507298
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.0017628518 0.8722504221 0.9904538380
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.6096921821 0.7184255164 0.0002928051
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.4665498960 0.8911193938 0.6240892800
## Joint P-value (lower = worse): 0.002977 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.5162 -0.2428 -0.7280
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.1809 0.6069 -0.5319
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.2017 -1.0686 0.7139
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 1.2511 0.4922 0.9422
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.6057282 0.8081943 0.4666414
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.8564672 0.5439399 0.5948135
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.8401430 0.2852713 0.4752824
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.2109145 0.6225513 0.3461031
## Joint P-value (lower = worse): 0.6406796 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.002678 -0.386209 -0.118569
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.420251 1.123652 -0.090563
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.185214 -0.070434 1.080202
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.102996 0.374554 0.241275
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.9978631 0.6993422 0.9056168
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.6743023 0.2611608 0.9278397
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.8530611 0.9438485 0.2800523
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.9179661 0.7079918 0.8093421
## Joint P-value (lower = worse): 0.7467302 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.466752 0.871471 -1.035353
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.002852 -0.761483 -0.541040
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.243867 -0.178901 -1.052950
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.714575 0.709418 0.489376
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.6406771 0.3834969 0.3005040
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.9977241 0.4463685 0.5884800
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.8073337 0.8580156 0.2923639
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.4748717 0.4780648 0.6245755
## Joint P-value (lower = worse): 0.7237065 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.54419 0.49282 -0.48359
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.50540 -0.66043 -0.10413
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.38734 -1.43615 1.06199
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -0.04211 -0.76635 -1.33637
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.5863130 0.6221375 0.6286787
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.6132759 0.5089774 0.9170661
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.6985036 0.1509594 0.2882424
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.9664080 0.4434693 0.1814275
## Joint P-value (lower = worse): 0.4969747 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.0870 0.4354 -1.6327
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.1100 1.1555 0.1715
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.2616 -1.3120 0.6957
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.4117 -0.5344 1.0423
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.9306745 0.6632484 0.1025316
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.9123814 0.2478658 0.8638537
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.7936411 0.1895239 0.4865913
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.6805511 0.5930842 0.2972517
## Joint P-value (lower = worse): 0.3099714 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Summary of model fit
##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + concurrent +
## nodematch("race..wa", diff = TRUE) + nodematch("region",
## diff = FALSE) + absdiff("sqrt.age") + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x560961f587d0>
##
## Iterations: 68 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -13.02786 0.17477 0 < 1e-04 ***
## nodefactor.deg.main.1 -0.11396 0.02865 0 < 1e-04 ***
## nodefactor.race..wa.B 0.59103 0.13366 0 < 1e-04 ***
## nodefactor.race..wa.H 0.79847 0.14449 0 < 1e-04 ***
## nodefactor.region.EW 0.52254 0.04046 0 < 1e-04 ***
## nodefactor.region.OW 0.14324 0.02267 0 < 1e-04 ***
## concurrent 2.49763 0.06363 0 < 1e-04 ***
## nodematch.race..wa.B -0.58749 0.37322 0 0.115466
## nodematch.race..wa.H -0.32257 0.20838 0 0.121626
## nodematch.race..wa.O 0.53350 0.15499 0 0.000577 ***
## nodematch.region 1.79899 0.05832 0 < 1e-04 ***
## absdiff.sqrt.age -0.54408 0.03241 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Network diagnostics
(dx_pers <- netdx(fit.p, nsims = 10, nsteps = 1000, ncores = 4, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5), set.control.ergm = control.simulate.ergm(MCMC.interval = 1e+5, MCMC.burnin = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2162.223 0.072 76.446
## nodefactor.deg.main.1 1699.000 1812.677 0.067 75.448
## nodefactor.race..wa.B 285.517 304.237 0.066 21.429
## nodefactor.race..wa.H 605.340 639.074 0.056 29.909
## nodefactor.region.EW 368.375 397.672 0.080 35.643
## nodefactor.region.OW 1178.294 1273.098 0.080 65.239
## concurrent 1384.000 1472.923 0.064 64.430
## nodematch.race..wa.B 8.480 8.825 0.041 3.189
## nodematch.race..wa.H 51.181 52.623 0.028 7.053
## nodematch.race..wa.O 1247.081 1344.654 0.078 58.028
## nodematch.region 1614.000 1726.959 0.070 62.224
## absdiff.sqrt.age 1664.841 1786.371 0.073 73.060
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.566 -0.032 30.065
## Pct Edges Diss 0.032 0.032 0.000 0.004plot(dx_pers, type="formation")
plot(dx_pers, type="duration")
plot(dx_pers, type="dissolution")
Persistent model, longer duration
This model uses balanced statistics for racial/ethnic mixing and race/ethnicity-specific degree, imposes the constraint on degree using the argument: constraints = ~bd(maxout = 2), and assumes 80% regional homophily. In this model we specify a duration of 100 time steps to test whether fit issues are due to the duration being too short for edapprox.
Model terms and control settings
Terms and constraints| Terms and constraints | Full model |
|---|---|
| edges | 2017.5 |
| nodefactor.deg.main.1 | 1699.0 |
| nodefactor.race..wa.B | 285.5 |
| nodefactor.race..wa.H | 605.3 |
| nodefactor.region.EW | 368.4 |
| nodefactor.region.OW | 1178.3 |
| concurrent | 1384.0 |
| nodematch.race..wa.B | 8.5 |
| nodematch.race..wa.H | 51.2 |
| nodematch.race..wa.O | 1247.1 |
| nodematch.region | 1614.0 |
| absdiff.sqrt.age | 1664.8 |
| nodematch.role.class.I | -Inf |
| nodematch.role.class.R | -Inf |
Control settings
set.control.ergm = control.ergm(MCMC.interval = 1e+5,
MCMC.samplesize = 7500,
MCMC.burnin = 1e+6,
MPLE.max.dyad.types = 1e+7,
init.method = "zeros",
MCMLE.maxit = 400,
parallel = np/2,
parallel.type="PSOCK"))MCMC diagnostics
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 3.95413 58.483 0.33765 0.38167
## nodefactor.deg.main.1 3.83660 61.191 0.35328 0.39136
## nodefactor.race..wa.B 0.08123 19.482 0.11248 0.13037
## nodefactor.race..wa.H 1.39317 29.682 0.17137 0.20420
## nodefactor.region.EW 0.70433 29.070 0.16784 0.22563
## nodefactor.region.OW 4.15640 58.101 0.33544 0.38806
## concurrent 4.25547 52.338 0.30218 0.34255
## nodematch.race..wa.B 0.03132 2.948 0.01702 0.01930
## nodematch.race..wa.H 0.02690 7.360 0.04249 0.05628
## nodematch.race..wa.O 2.46925 44.458 0.25668 0.28883
## nodematch.region 1.71267 50.020 0.28879 0.33527
## absdiff.sqrt.age 3.58324 57.503 0.33199 0.35591
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -110.50 -35.500 3.5000 42.500 121.50
## nodefactor.deg.main.1 -115.00 -38.000 3.0000 45.000 125.00
## nodefactor.race..wa.B -37.52 -13.517 0.4832 13.483 38.48
## nodefactor.race..wa.H -56.34 -18.590 0.6600 21.660 59.66
## nodefactor.region.EW -55.37 -19.375 0.6250 19.625 59.63
## nodefactor.region.OW -110.29 -35.294 4.7064 42.706 119.71
## concurrent -98.00 -31.000 4.0000 39.000 108.00
## nodematch.race..wa.B -5.48 -2.480 -0.4798 1.520 6.52
## nodematch.race..wa.H -13.18 -5.181 -0.1815 4.819 14.82
## nodematch.race..wa.O -84.08 -27.081 1.9192 31.919 90.92
## nodematch.region -97.00 -32.000 2.0000 35.000 102.00
## absdiff.sqrt.age -108.59 -35.673 3.2889 42.585 116.41
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.main.1
## edges 1.00000000 0.81695112
## nodefactor.deg.main.1 0.81695112 1.00000000
## nodefactor.race..wa.B 0.40148519 0.31338716
## nodefactor.race..wa.H 0.54450156 0.48172703
## nodefactor.region.EW 0.38608243 0.31245166
## nodefactor.region.OW 0.61733039 0.44999140
## concurrent 0.95407962 0.77642179
## nodematch.race..wa.B 0.07149044 0.04911494
## nodematch.race..wa.H 0.17326336 0.16588998
## nodematch.race..wa.O 0.84430282 0.67573135
## nodematch.region 0.93032629 0.76492601
## absdiff.sqrt.age 0.84530667 0.69222137
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.40148519 0.54450156
## nodefactor.deg.main.1 0.31338716 0.48172703
## nodefactor.race..wa.B 1.00000000 0.18310402
## nodefactor.race..wa.H 0.18310402 1.00000000
## nodefactor.region.EW 0.09318073 0.33830988
## nodefactor.region.OW 0.20262168 0.32886831
## concurrent 0.38812233 0.52976052
## nodematch.race..wa.B 0.35710327 0.01015892
## nodematch.race..wa.H 0.01764166 0.56311675
## nodematch.race..wa.O 0.08165528 0.12006670
## nodematch.region 0.38824106 0.49133477
## absdiff.sqrt.age 0.33627704 0.46052388
## nodefactor.region.EW nodefactor.region.OW concurrent
## edges 0.38608243 0.61733039 0.95407962
## nodefactor.deg.main.1 0.31245166 0.44999140 0.77642179
## nodefactor.race..wa.B 0.09318073 0.20262168 0.38812233
## nodefactor.race..wa.H 0.33830988 0.32886831 0.52976052
## nodefactor.region.EW 1.00000000 0.10758199 0.36519158
## nodefactor.region.OW 0.10758199 1.00000000 0.57907042
## concurrent 0.36519158 0.57907042 1.00000000
## nodematch.race..wa.B 0.01208289 0.02391929 0.06894633
## nodematch.race..wa.H 0.16779221 0.10081102 0.17454172
## nodematch.race..wa.O 0.28250047 0.53927844 0.79936437
## nodematch.region 0.25555012 0.54198422 0.88826699
## absdiff.sqrt.age 0.32118875 0.51479851 0.80257508
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.0714904431 0.1732633556
## nodefactor.deg.main.1 0.0491149412 0.1658899775
## nodefactor.race..wa.B 0.3571032656 0.0176416648
## nodefactor.race..wa.H 0.0101589210 0.5631167455
## nodefactor.region.EW 0.0120828876 0.1677922106
## nodefactor.region.OW 0.0239192890 0.1008110235
## concurrent 0.0689463283 0.1745417166
## nodematch.race..wa.B 1.0000000000 0.0008591835
## nodematch.race..wa.H 0.0008591835 1.0000000000
## nodematch.race..wa.O 0.0026523982 0.0148416292
## nodematch.region 0.0707768261 0.1536062429
## absdiff.sqrt.age 0.0566932380 0.1495654934
## nodematch.race..wa.O nodematch.region
## edges 0.844302815 0.93032629
## nodefactor.deg.main.1 0.675731347 0.76492601
## nodefactor.race..wa.B 0.081655281 0.38824106
## nodefactor.race..wa.H 0.120066700 0.49133477
## nodefactor.region.EW 0.282500468 0.25555012
## nodefactor.region.OW 0.539278439 0.54198422
## concurrent 0.799364367 0.88826699
## nodematch.race..wa.B 0.002652398 0.07077683
## nodematch.race..wa.H 0.014841629 0.15360624
## nodematch.race..wa.O 1.000000000 0.78851415
## nodematch.region 0.788514150 1.00000000
## absdiff.sqrt.age 0.714120332 0.78749267
## absdiff.sqrt.age
## edges 0.84530667
## nodefactor.deg.main.1 0.69222137
## nodefactor.race..wa.B 0.33627704
## nodefactor.race..wa.H 0.46052388
## nodefactor.region.EW 0.32118875
## nodefactor.region.OW 0.51479851
## concurrent 0.80257508
## nodematch.race..wa.B 0.05669324
## nodematch.race..wa.H 0.14956549
## nodematch.race..wa.O 0.71412033
## nodematch.region 0.78749267
## absdiff.sqrt.age 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.085682845 0.045711863 0.118791078
## Lag 2e+05 0.056477935 0.039130463 0.041184045
## Lag 3e+05 0.034193396 0.016529833 0.006328359
## Lag 4e+05 -0.001864522 -0.016964937 -0.024009643
## Lag 5e+05 -0.031042450 -0.007740416 -0.016064005
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.156571970 0.21433868 0.125173598
## Lag 2e+05 0.056781528 0.10353863 0.041001334
## Lag 3e+05 0.021876307 0.02064468 0.030701950
## Lag 4e+05 0.012921019 0.01856869 0.002575357
## Lag 5e+05 0.004997416 0.01304699 -0.013323143
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.10336873 0.09004685 0.252506496
## Lag 2e+05 0.05109432 0.04082347 0.087372138
## Lag 3e+05 0.02714698 0.03125051 0.032693707
## Lag 4e+05 0.01471282 -0.01627382 0.015374207
## Lag 5e+05 -0.02706179 0.00138370 -0.005008358
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.06953737 0.114426800 0.054459762
## Lag 2e+05 0.04297796 0.050936958 0.057109482
## Lag 3e+05 0.02261646 0.060898614 0.025839664
## Lag 4e+05 -0.01350893 0.004854153 -0.001447785
## Lag 5e+05 -0.02401619 -0.026477953 -0.025154838
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000e+00
## Lag 1e+05 0.123579977 0.117326110 1.144025e-01
## Lag 2e+05 0.016137638 0.002490185 2.919955e-02
## Lag 3e+05 0.002548453 -0.009515472 -3.888216e-04
## Lag 4e+05 0.004879543 -0.002579730 -4.537049e-05
## Lag 5e+05 0.023424076 0.034067754 -2.354112e-02
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.17547381 0.261461648 0.139325652
## Lag 2e+05 0.05775302 0.087958569 0.033671019
## Lag 3e+05 0.01494480 0.031220894 0.007242230
## Lag 4e+05 0.03487495 0.022938206 0.008464537
## Lag 5e+05 -0.03959400 0.001133238 0.004235455
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.1346043675 0.100901914 0.238166886
## Lag 2e+05 0.0199261072 -0.008225335 0.109711792
## Lag 3e+05 -0.0012436239 -0.019779872 0.021109740
## Lag 4e+05 -0.0001296406 -0.012964820 0.030727110
## Lag 5e+05 0.0256964874 0.019672376 -0.009319422
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0762686108 0.148821713 0.060627040
## Lag 2e+05 0.0111308037 0.011395857 0.005548423
## Lag 3e+05 0.0071388467 0.005475015 0.014450688
## Lag 4e+05 -0.0002369829 0.006827491 0.008523243
## Lag 5e+05 0.0433537301 0.030848420 0.053646716
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.121887814 0.113310538 0.099914237
## Lag 2e+05 0.003863130 -0.004100551 0.025045571
## Lag 3e+05 -0.011704546 -0.013045641 -0.004205328
## Lag 4e+05 -0.023388729 -0.013316007 0.002853317
## Lag 5e+05 0.003465713 0.011906041 0.008202142
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.17244610 0.24488872 0.122020943
## Lag 2e+05 0.04673811 0.09317131 0.047136436
## Lag 3e+05 -0.01235923 0.04495071 0.005460002
## Lag 4e+05 -0.02438436 0.03420307 -0.015404391
## Lag 5e+05 -0.02206323 0.04163887 -0.002862462
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.132216743 0.087138467 0.219339824
## Lag 2e+05 -0.004323050 0.010312171 0.108903112
## Lag 3e+05 0.001787012 0.016927477 0.041332016
## Lag 4e+05 -0.020513847 0.007247538 0.040369395
## Lag 5e+05 0.002000932 0.004674099 0.007037572
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.0000000000 1.00000000
## Lag 1e+05 0.097343865 0.1333292983 0.07655833
## Lag 2e+05 0.002803524 0.0119613271 0.01003141
## Lag 3e+05 -0.012123023 -0.0005602448 -0.04084131
## Lag 4e+05 0.002844179 -0.0195493535 -0.03223402
## Lag 5e+05 0.024671399 -0.0081355252 -0.01059809
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.09761441 0.079008398 0.10687420
## Lag 2e+05 0.02442334 0.034131895 0.04368658
## Lag 3e+05 0.03585876 0.037662603 0.01991004
## Lag 4e+05 0.01655672 0.003957693 0.01250930
## Lag 5e+05 0.03043562 0.008002627 0.01437945
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.14839434 0.23821895 0.132088557
## Lag 2e+05 0.05304535 0.09125455 0.053296938
## Lag 3e+05 0.02377460 0.05252177 0.024915636
## Lag 4e+05 0.01900053 0.06028238 0.032666797
## Lag 5e+05 0.02586823 0.03225883 0.006520256
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.10741841 0.093551228 0.22520194
## Lag 2e+05 0.02944774 0.008299961 0.09627098
## Lag 3e+05 0.03132741 -0.003151342 0.03446852
## Lag 4e+05 0.01233020 -0.003187537 0.01451638
## Lag 5e+05 0.02048104 -0.010275168 0.01131925
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.07302536 0.11818127 0.061582487
## Lag 2e+05 0.04423949 0.01901946 0.022401681
## Lag 3e+05 0.02479247 0.04710048 0.020811313
## Lag 4e+05 0.02838893 0.02147592 0.021026855
## Lag 5e+05 0.03045485 0.01848787 0.004192659
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.080294808 0.081253651 0.124443553
## Lag 2e+05 0.008981984 0.012996489 0.031839810
## Lag 3e+05 0.008719587 0.006750415 0.047522466
## Lag 4e+05 -0.015922925 -0.009206460 0.015547876
## Lag 5e+05 0.015078422 -0.002429443 0.007650274
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.0000000000
## Lag 1e+05 0.133812295 0.23368813 0.1255646380
## Lag 2e+05 0.065243635 0.06847331 0.0301664659
## Lag 3e+05 0.011726412 0.03631259 0.0088767991
## Lag 4e+05 -0.006850757 0.03166637 -0.0005803247
## Lag 5e+05 -0.017639542 0.02398633 -0.0198106094
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.092756337 0.14109689 0.25889547
## Lag 2e+05 0.024724979 0.03872845 0.11555294
## Lag 3e+05 0.016071472 0.02039292 0.07192408
## Lag 4e+05 -0.015607116 0.03731360 0.02645284
## Lag 5e+05 0.008834721 0.02149058 -0.01314329
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.0000000000 1.000000000
## Lag 1e+05 0.07284264 0.1000939837 0.058098366
## Lag 2e+05 0.01076183 0.0169675703 -0.001373238
## Lag 3e+05 0.00887251 0.0212780176 -0.008301660
## Lag 4e+05 -0.01395616 -0.0007199413 -0.015415790
## Lag 5e+05 0.02381727 0.0241226956 0.003318379
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000 1.00000000
## Lag 1e+05 0.1067649394 0.100675236 0.10296248
## Lag 2e+05 0.0206891261 0.015226463 0.02464623
## Lag 3e+05 0.0043941599 0.002067761 0.04426896
## Lag 4e+05 0.0008297646 0.004514396 0.01622341
## Lag 5e+05 0.0422701010 0.033888656 0.04497334
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.15300693 0.23013847 0.169858120
## Lag 2e+05 0.03972834 0.08929983 0.043224185
## Lag 3e+05 0.02173069 0.05160093 0.009026793
## Lag 4e+05 0.03796910 0.04945323 0.031425537
## Lag 5e+05 0.03097387 0.01795526 0.024551042
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.103910331 0.1173943986 0.238230001
## Lag 2e+05 0.014907079 0.0077179771 0.110454082
## Lag 3e+05 -0.002172196 0.0366024448 0.055274531
## Lag 4e+05 0.007009171 0.0272581179 0.033148759
## Lag 5e+05 0.031241628 0.0007042865 0.001644066
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.075201810 0.140735039 0.071162586
## Lag 2e+05 0.003423980 0.018133941 -0.017044156
## Lag 3e+05 -0.010671075 -0.002649672 -0.006551417
## Lag 4e+05 -0.010596997 0.011640094 0.002965720
## Lag 5e+05 -0.001215687 0.037289880 0.042296072
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.095851996 0.095424884 0.1151225897
## Lag 2e+05 0.024526880 0.019231259 -0.0001005062
## Lag 3e+05 0.007513803 0.013987772 0.0150972588
## Lag 4e+05 -0.022788922 0.001844952 -0.0198731207
## Lag 5e+05 -0.017869922 -0.011498302 -0.0144098769
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.132730046 0.24824679 0.11690544
## Lag 2e+05 0.026681113 0.10185999 0.03094174
## Lag 3e+05 0.035967231 0.06221028 0.01482084
## Lag 4e+05 0.024120769 0.03940781 -0.02055949
## Lag 5e+05 0.007369577 0.04182918 0.00215649
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.126356330 0.09690529 0.20585411
## Lag 2e+05 0.028003508 0.01134319 0.07069444
## Lag 3e+05 0.009696162 0.00247933 0.03162854
## Lag 4e+05 -0.015329507 0.01188419 0.03147746
## Lag 5e+05 -0.012476420 -0.01881904 0.03196902
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.091651891 0.13305617 0.04907682
## Lag 2e+05 0.034472681 0.03227193 0.01714870
## Lag 3e+05 0.005767669 0.01202773 0.01456785
## Lag 4e+05 -0.012496216 -0.02755554 -0.02572536
## Lag 5e+05 -0.005952078 -0.01478503 -0.01994673
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 0.0961955747 0.082580966 0.1030938883
## Lag 2e+05 0.0434041434 0.014853972 0.0655928725
## Lag 3e+05 0.0141038844 0.014382104 -0.0060511451
## Lag 4e+05 -0.0002502332 0.011126890 0.0002523183
## Lag 5e+05 -0.0152538354 -0.007613119 -0.0071486434
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.12074964 0.225909277 0.129107567
## Lag 2e+05 0.04950480 0.082084185 0.032508811
## Lag 3e+05 0.02287368 0.045015971 0.003913842
## Lag 4e+05 0.03272591 0.046723957 0.002300074
## Lag 5e+05 0.02041754 0.008699385 -0.016015061
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.114511212 0.1185734207 0.213600239
## Lag 2e+05 0.050427165 0.0002061981 0.078918158
## Lag 3e+05 0.008864216 0.0269455614 0.022038718
## Lag 4e+05 0.001442323 -0.0088819067 0.008081591
## Lag 5e+05 -0.008987872 0.0399991182 0.021556636
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.080107715 0.1170513330 0.060128944
## Lag 2e+05 0.046917703 0.0502168730 0.012268727
## Lag 3e+05 0.007272004 -0.0021968962 -0.008851137
## Lag 4e+05 -0.002408410 0.0008883817 -0.012534753
## Lag 5e+05 -0.005081352 -0.0113146851 -0.016591129
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.36237 -0.32284 -0.98615
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -1.19858 -0.22881 0.23795
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.66543 -1.62244 0.04647
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.38972 -1.00738 -0.34225
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7170779 0.7468154 0.3240609
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.2306921 0.8190128 0.8119224
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.5057782 0.1047084 0.9629386
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.6967399 0.3137499 0.7321644
## Joint P-value (lower = worse): 0.3294881 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.4505 -1.2505 1.3116
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 1.0588 0.3331 0.8757
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.1808 -0.1009 -0.1354
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -1.7114 -0.4046 -0.2801
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.65233687 0.21110115 0.18966504
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.28968100 0.73905908 0.38119483
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.85655486 0.91965907 0.89229473
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.08700113 0.68574529 0.77943590
## Joint P-value (lower = worse): 0.3741391 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.84659 0.09812 0.09258
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.42024 -0.69875 0.85994
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.88795 1.09340 0.46326
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 1.08306 0.76147 0.84166
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.3972218 0.9218399 0.9262372
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.6743096 0.4847097 0.3898243
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.3745693 0.2742186 0.6431811
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.2787804 0.4463766 0.3999788
## Joint P-value (lower = worse): 0.8173081 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.5159 -0.4397 -0.1338
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.9074 3.2903 -1.3245
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.5285 -0.4088 0.4173
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -0.6754 -1.1862 -0.9801
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.605940268 0.660174741 0.893579722
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.364190007 0.001000918 0.185337445
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.597127852 0.682697854 0.676475259
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.499413782 0.235562116 0.327026022
## Joint P-value (lower = worse): 0.03116766 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.5032 0.2661 -0.4158
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 1.2664 0.1697 0.1538
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.4806 1.8384 0.9211
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -0.9217 -0.2323 -0.6774
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.61483289 0.79015234 0.67755219
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.20536741 0.86523928 0.87779427
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.63083413 0.06600915 0.35699347
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.35668252 0.81631805 0.49817835
## Joint P-value (lower = worse): 0.4774389 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.5937 -0.1780 -0.2426
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.2752 -0.3233 1.1075
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.4357 0.1428 -0.2920
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.7656 0.9536 1.6373
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.5526890 0.8587010 0.8083029
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.7831340 0.7464493 0.2680934
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.6630475 0.8864118 0.7703102
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.4439245 0.3402672 0.1015654
## Joint P-value (lower = worse): 0.6916566 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.189987 0.159961 0.244522
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.972309 0.034637 -0.201594
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.482481 2.231730 -0.479812
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.711795 0.006289 -0.053153
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.84931968 0.87291180 0.80682694
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.33089690 0.97236882 0.84023428
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.62946434 0.02563284 0.63136098
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.47659192 0.99498209 0.95760984
## Joint P-value (lower = worse): 0.6855451 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.33443 0.27932 0.91810
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.27296 0.05707 1.56915
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.27558 0.76089 -2.02026
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -0.56085 0.27438 0.14962
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.73805476 0.77999615 0.35856708
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.78488212 0.95449245 0.11661281
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.78287231 0.44672277 0.04335653
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.57489883 0.78379342 0.88106277
## Joint P-value (lower = worse): 0.3684676 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Summary of model fit
##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + concurrent +
## nodematch("race..wa", diff = TRUE) + nodematch("region",
## diff = FALSE) + absdiff("sqrt.age") + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x56098681ffc0>
##
## Iterations: 68 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -13.02679 0.17613 0 < 1e-04 ***
## nodefactor.deg.main.1 -0.11365 0.02868 0 < 1e-04 ***
## nodefactor.race..wa.B 0.59123 0.13507 0 < 1e-04 ***
## nodefactor.race..wa.H 0.79745 0.14531 0 < 1e-04 ***
## nodefactor.region.EW 0.52276 0.04092 0 < 1e-04 ***
## nodefactor.region.OW 0.14334 0.02288 0 < 1e-04 ***
## concurrent 2.49672 0.06397 0 < 1e-04 ***
## nodematch.race..wa.B -0.59149 0.37575 0 0.115455
## nodematch.race..wa.H -0.32325 0.20749 0 0.119263
## nodematch.race..wa.O 0.53298 0.15624 0 0.000646 ***
## nodematch.region 1.79911 0.05822 0 < 1e-04 ***
## absdiff.sqrt.age -0.54424 0.03257 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 100
## Crude Coefficient: 4.59512
## Mortality/Exit Rate: 0
## Adjusted Coefficient: 4.59512Network diagnostics
(dx_pers2 <- netdx(fit.p2, nsims = 10, nsteps = 1000, ncores = 4, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5), set.control.ergm = control.simulate.ergm(MCMC.interval = 1e+5, MCMC.burnin = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2027.783 0.005 61.339
## nodefactor.deg.main.1 1699.000 1696.914 -0.001 62.353
## nodefactor.race..wa.B 285.517 288.995 0.012 19.562
## nodefactor.race..wa.H 605.340 611.186 0.010 35.107
## nodefactor.region.EW 368.375 363.382 -0.014 30.612
## nodefactor.region.OW 1178.294 1198.132 0.017 56.142
## concurrent 1384.000 1382.777 -0.001 54.994
## nodematch.race..wa.B 8.480 8.795 0.037 3.058
## nodematch.race..wa.H 51.181 52.168 0.019 6.983
## nodematch.race..wa.O 1247.081 1250.270 0.003 42.275
## nodematch.region 1614.000 1624.464 0.006 50.383
## absdiff.sqrt.age 1664.841 1670.198 0.003 60.226
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1e+02 88.83 -0.112 86.882
## Pct Edges Diss 1e-02 0.01 0.002 0.002plot(dx_pers2, type="formation")
plot(dx_pers2, type="duration")
plot(dx_pers2, type="dissolution")